Multi-scale searching machine to detect the cosmic strings network

The initial conditions for large-scale structures is a mysterious subject in cosmology. The inflationary paradigm is widely accepted scenario for seeding the structure in the Universe. However, there is room for alternative scenarios not only from observational points of view but also from theoretical approaches. Cosmic topological defects formed during phase transition in the very early universe are theoretically well-motivated. Cosmic strings network is parameterized by the energy scale of the phase transition represented by G?. The cosmic string can leave the imprint on the CMB stochastic field leading to emerging additional stochasticity behavior in the CMB map. In this talk, I will rely on the stochasticity nature of CMB superimposed by cosmic strings network due to Gott-Kaiser-Stebbins phenomenon. Some topological and geometrical measures accompanying multi-scale edge-detection algorithm to examine the minimum value of G? for which, our pipeline is able to detect the cosmic strings network incorporating anticipated systematic noises for some surveys, will be introduced. On the noiseless sky maps with an angular resolution of 0.9?, we show that our pipeline detects cosmic string with G? as low as G??4.3◊10?10. At the same resolution, but with a noise level typical to a CMB-S4 phase II experiment, the detection threshold would be to G??1.2◊10?7. We also explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms to determine the feature importance of some topological and geometrical measures in detecting cosmic strings. Such approach opens new insight into utilizing prior information for detecting exotic features in a stochastic field.